A Practical Framework Toward Prediction of Breaking Force and Disintegration of Tablet Formulations Using Machine Learning Tools

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• 作者：Ilgaz Akseli ; ^{iakseli@celgene.com} ; Jingjin Xie ; Leon Schultz ; Nadia Ladyzhynsky ; Tommasina Bramante ; Xiaorong He ; Rich Deanne ; Keith R. Horspool ; Robert Schwabe
• 关键词：predictive modeling ; tablet formulation ; tablet breaking force ; disintegration time ; nondestructive testing ; neuroevolution
• 刊名：Journal of Pharmaceutical Sciences
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：106
• 期：1
• 页码：234-247
• 全文大小：3711 K
• 卷排序：106

文摘

Enabling the paradigm of quality by design requires the ability to quantitatively correlate material properties and process variables to measureable product performance attributes. Conventional, quality-by-test methods for determining tablet breaking force and disintegration time usually involve destructive tests, which consume significant amount of time and labor and provide limited information. Recent advances in material characterization, statistical analysis, and machine learning have provided multiple tools that have the potential to develop nondestructive, fast, and accurate approaches in drug product development. In this work, a methodology to predict the breaking force and disintegration time of tablet formulations using nondestructive ultrasonics and machine learning tools was developed. The input variables to the model include intrinsic properties of formulation and extrinsic process variables influencing the tablet during manufacturing. The model has been applied to predict breaking force and disintegration time using small quantities of active pharmaceutical ingredient and prototype formulation designs. The novel approach presented is a step forward toward rational design of a robust drug product based on insight into the performance of common materials during formulation and process development. It may also help expedite drug product development timeline and reduce active pharmaceutical ingredient usage while improving efficiency of the overall process.